Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations23983
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory120.0 B

Variable types

Numeric8
Boolean1
Categorical6
DateTime1

Alerts

age is highly overall correlated with job_stability_missingHigh correlation
cocunut is highly overall correlated with current_balance_eurHigh correlation
current_balance_eur is highly overall correlated with cocunut and 1 other fieldsHigh correlation
cust_income is highly overall correlated with cust_income_logHigh correlation
cust_income_log is highly overall correlated with cust_incomeHigh correlation
employment is highly overall correlated with job_stability_missingHigh correlation
job_stability_missing is highly overall correlated with age and 2 other fieldsHigh correlation
job_stability_years is highly overall correlated with job_stability_missingHigh correlation
mortgage_yn is highly overall correlated with current_balance_eurHigh correlation
mortgage_yn is highly imbalanced (90.1%) Imbalance
address_stability_missing is highly imbalanced (92.8%) Imbalance
cust_income is highly skewed (γ1 = 21.6784989) Skewed
cocunut has unique values Unique
years_with_bank has 525 (2.2%) zeros Zeros
current_balance_eur has 2911 (12.1%) zeros Zeros

Reproduction

Analysis started2025-05-17 15:10:38.835277
Analysis finished2025-05-17 15:11:08.118253
Duration29.28 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

cocunut
Real number (ℝ)

High correlation  Unique 

Distinct23983
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39969.698
Minimum1
Maximum79998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:08.399256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3943.1
Q120076
median40066
Q359778
95-th percentile75857.9
Maximum79998
Range79997
Interquartile range (IQR)39702

Descriptive statistics

Standard deviation22996.273
Coefficient of variation (CV)0.57534268
Kurtosis-1.1887028
Mean39969.698
Median Absolute Deviation (MAD)19845
Skewness-0.0037161146
Sum9.5859326 × 108
Variance5.2882857 × 108
MonotonicityStrictly increasing
2025-05-17T17:11:08.781253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
53107 1
 
< 0.1%
53133 1
 
< 0.1%
53132 1
 
< 0.1%
53131 1
 
< 0.1%
53126 1
 
< 0.1%
53120 1
 
< 0.1%
53113 1
 
< 0.1%
53111 1
 
< 0.1%
53110 1
 
< 0.1%
Other values (23973) 23973
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
21 1
< 0.1%
24 1
< 0.1%
29 1
< 0.1%
33 1
< 0.1%
ValueCountFrequency (%)
79998 1
< 0.1%
79985 1
< 0.1%
79983 1
< 0.1%
79982 1
< 0.1%
79979 1
< 0.1%
79976 1
< 0.1%
79974 1
< 0.1%
79970 1
< 0.1%
79965 1
< 0.1%
79961 1
< 0.1%

mortgage_yn
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
False
23677 
True
 
306
ValueCountFrequency (%)
False 23677
98.7%
True 306
 
1.3%
2025-05-17T17:11:09.054258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

age
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.004378
Minimum20
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:09.310255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile28
Q139
median49
Q360
95-th percentile69
Maximum92
Range72
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.976205
Coefficient of variation (CV)0.26479685
Kurtosis-0.91302991
Mean49.004378
Median Absolute Deviation (MAD)11
Skewness-0.0066111492
Sum1175272
Variance168.38189
MonotonicityNot monotonic
2025-05-17T17:11:09.819253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 685
 
2.9%
44 632
 
2.6%
64 629
 
2.6%
40 615
 
2.6%
56 613
 
2.6%
62 611
 
2.5%
41 611
 
2.5%
38 602
 
2.5%
45 592
 
2.5%
65 592
 
2.5%
Other values (63) 17801
74.2%
ValueCountFrequency (%)
20 5
 
< 0.1%
21 17
 
0.1%
22 74
 
0.3%
23 110
 
0.5%
24 157
0.7%
25 199
0.8%
26 219
0.9%
27 272
1.1%
28 276
1.2%
29 315
1.3%
ValueCountFrequency (%)
92 2
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
89 3
 
< 0.1%
88 1
 
< 0.1%
87 3
 
< 0.1%
86 5
< 0.1%
85 6
< 0.1%
84 6
< 0.1%
83 12
0.1%

years_with_bank
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3708043
Minimum0
Maximum40
Zeros525
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:10.104257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median9
Q311
95-th percentile13
Maximum40
Range40
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.3205683
Coefficient of variation (CV)0.58617325
Kurtosis-1.295605
Mean7.3708043
Median Absolute Deviation (MAD)3
Skewness-0.15057594
Sum176774
Variance18.66731
MonotonicityNot monotonic
2025-05-17T17:11:10.394255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
11 3677
15.3%
12 2604
10.9%
2 2379
9.9%
10 2117
8.8%
1 2019
8.4%
4 1946
8.1%
3 1686
7.0%
9 1514
 
6.3%
13 1228
 
5.1%
5 1022
 
4.3%
Other values (8) 3791
15.8%
ValueCountFrequency (%)
0 525
 
2.2%
1 2019
8.4%
2 2379
9.9%
3 1686
7.0%
4 1946
8.1%
5 1022
4.3%
6 792
 
3.3%
7 669
 
2.8%
8 907
 
3.8%
9 1514
6.3%
ValueCountFrequency (%)
40 1
 
< 0.1%
16 5
 
< 0.1%
15 291
 
1.2%
14 601
 
2.5%
13 1228
 
5.1%
12 2604
10.9%
11 3677
15.3%
10 2117
8.8%
9 1514
6.3%
8 907
 
3.8%

marital_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.5 KiB
M
17061 
S
4227 
D
 
1364
W
 
1331

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23983
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowS

Common Values

ValueCountFrequency (%)
M 17061
71.1%
S 4227
 
17.6%
D 1364
 
5.7%
W 1331
 
5.5%

Length

2025-05-17T17:11:10.733255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:11.010438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 17061
71.1%
s 4227
 
17.6%
d 1364
 
5.7%
w 1331
 
5.5%

Most occurring characters

ValueCountFrequency (%)
M 17061
71.1%
S 4227
 
17.6%
D 1364
 
5.7%
W 1331
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 17061
71.1%
S 4227
 
17.6%
D 1364
 
5.7%
W 1331
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 17061
71.1%
S 4227
 
17.6%
D 1364
 
5.7%
W 1331
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 17061
71.1%
S 4227
 
17.6%
D 1364
 
5.7%
W 1331
 
5.5%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.5 KiB
HGH
15987 
BCR
6629 
OTH
 
1174
MAS
 
193

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters71949
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHGH
2nd rowHGH
3rd rowBCR
4th rowBCR
5th rowMAS

Common Values

ValueCountFrequency (%)
HGH 15987
66.7%
BCR 6629
27.6%
OTH 1174
 
4.9%
MAS 193
 
0.8%

Length

2025-05-17T17:11:11.383438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:11.619367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hgh 15987
66.7%
bcr 6629
27.6%
oth 1174
 
4.9%
mas 193
 
0.8%

Most occurring characters

ValueCountFrequency (%)
H 33148
46.1%
G 15987
22.2%
B 6629
 
9.2%
C 6629
 
9.2%
R 6629
 
9.2%
O 1174
 
1.6%
T 1174
 
1.6%
M 193
 
0.3%
A 193
 
0.3%
S 193
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 33148
46.1%
G 15987
22.2%
B 6629
 
9.2%
C 6629
 
9.2%
R 6629
 
9.2%
O 1174
 
1.6%
T 1174
 
1.6%
M 193
 
0.3%
A 193
 
0.3%
S 193
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 33148
46.1%
G 15987
22.2%
B 6629
 
9.2%
C 6629
 
9.2%
R 6629
 
9.2%
O 1174
 
1.6%
T 1174
 
1.6%
M 193
 
0.3%
A 193
 
0.3%
S 193
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 33148
46.1%
G 15987
22.2%
B 6629
 
9.2%
C 6629
 
9.2%
R 6629
 
9.2%
O 1174
 
1.6%
T 1174
 
1.6%
M 193
 
0.3%
A 193
 
0.3%
S 193
 
0.3%

employment
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.5 KiB
PVE
10743 
STE
6709 
RET
6100 
MISC
 
431

Length

Max length4
Median length3
Mean length3.0179711
Min length3

Characters and Unicode

Total characters72380
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPVE
2nd rowMISC
3rd rowSTE
4th rowMISC
5th rowPVE

Common Values

ValueCountFrequency (%)
PVE 10743
44.8%
STE 6709
28.0%
RET 6100
25.4%
MISC 431
 
1.8%

Length

2025-05-17T17:11:11.932368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:12.182368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pve 10743
44.8%
ste 6709
28.0%
ret 6100
25.4%
misc 431
 
1.8%

Most occurring characters

ValueCountFrequency (%)
E 23552
32.5%
T 12809
17.7%
P 10743
14.8%
V 10743
14.8%
S 7140
 
9.9%
R 6100
 
8.4%
M 431
 
0.6%
I 431
 
0.6%
C 431
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 23552
32.5%
T 12809
17.7%
P 10743
14.8%
V 10743
14.8%
S 7140
 
9.9%
R 6100
 
8.4%
M 431
 
0.6%
I 431
 
0.6%
C 431
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 23552
32.5%
T 12809
17.7%
P 10743
14.8%
V 10743
14.8%
S 7140
 
9.9%
R 6100
 
8.4%
M 431
 
0.6%
I 431
 
0.6%
C 431
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 23552
32.5%
T 12809
17.7%
P 10743
14.8%
V 10743
14.8%
S 7140
 
9.9%
R 6100
 
8.4%
M 431
 
0.6%
I 431
 
0.6%
C 431
 
0.6%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.5 KiB
F
12105 
M
11878 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23983
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 12105
50.5%
M 11878
49.5%

Length

2025-05-17T17:11:12.453366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:12.633371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 12105
50.5%
m 11878
49.5%

Most occurring characters

ValueCountFrequency (%)
F 12105
50.5%
M 11878
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 12105
50.5%
M 11878
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 12105
50.5%
M 11878
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 12105
50.5%
M 11878
49.5%

cust_income
Real number (ℝ)

High correlation  Skewed 

Distinct18823
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.51248
Minimum0
Maximum25741.92
Zeros85
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:12.909368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile166.56292
Q1212.30454
median287.35569
Q3406.10027
95-th percentile854.80374
Maximum25741.92
Range25741.92
Interquartile range (IQR)193.79573

Descriptive statistics

Standard deviation486.25544
Coefficient of variation (CV)1.2745466
Kurtosis847.53263
Mean381.51248
Median Absolute Deviation (MAD)87.355692
Skewness21.678499
Sum9149813.9
Variance236444.35
MonotonicityNot monotonic
2025-05-17T17:11:13.278371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230.7692308 217
 
0.9%
307.6923077 191
 
0.8%
269.2307692 186
 
0.8%
192.3076923 178
 
0.7%
346.1538462 121
 
0.5%
246.1538462 105
 
0.4%
384.6153846 105
 
0.4%
292.3076923 93
 
0.4%
0 85
 
0.4%
215.3846154 84
 
0.4%
Other values (18813) 22618
94.3%
ValueCountFrequency (%)
0 85
0.4%
0.384615385 1
 
< 0.1%
0.769230769 13
 
0.1%
7.692307692 2
 
< 0.1%
73.08769231 1
 
< 0.1%
81.36392308 1
 
< 0.1%
86.78230769 1
 
< 0.1%
93.63184615 1
 
< 0.1%
93.69484615 1
 
< 0.1%
95.46153846 1
 
< 0.1%
ValueCountFrequency (%)
25741.92 1
< 0.1%
24954.29169 1
< 0.1%
20165.67908 1
< 0.1%
17135.16154 1
< 0.1%
13234.37692 1
< 0.1%
12859.33077 1
< 0.1%
11531.73077 1
< 0.1%
10492.51708 1
< 0.1%
10006.94569 1
< 0.1%
8289.723077 1
< 0.1%
Distinct4142
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size187.5 KiB
Minimum1976-07-17 00:00:00
Maximum2016-10-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-17T17:11:13.665366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:14.092367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

current_balance_eur
Real number (ℝ)

High correlation  Zeros 

Distinct18415
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1960.8545
Minimum0
Maximum187940.65
Zeros2911
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:14.473367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1107.67758
median428.067
Q32307.6923
95-th percentile6662.9845
Maximum187940.65
Range187940.65
Interquartile range (IQR)2200.0147

Descriptive statistics

Standard deviation5023.2347
Coefficient of variation (CV)2.561758
Kurtosis255.34186
Mean1960.8545
Median Absolute Deviation (MAD)428.067
Skewness12.439976
Sum47027173
Variance25232886
MonotonicityNot monotonic
2025-05-17T17:11:14.830368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2911
 
12.1%
2269.547846 148
 
0.6%
2228.960462 133
 
0.6%
2250.234 132
 
0.6%
2288.700154 126
 
0.5%
2189.095846 120
 
0.5%
2307.692308 115
 
0.5%
2268.491692 114
 
0.5%
76.92307692 90
 
0.4%
4500.468231 65
 
0.3%
Other values (18405) 20029
83.5%
ValueCountFrequency (%)
0 2911
12.1%
0.005615385 1
 
< 0.1%
0.012615385 1
 
< 0.1%
0.020538462 1
 
< 0.1%
0.048846154 1
 
< 0.1%
0.049692308 1
 
< 0.1%
0.054538462 1
 
< 0.1%
0.055923077 1
 
< 0.1%
0.075076923 1
 
< 0.1%
0.115384615 1
 
< 0.1%
ValueCountFrequency (%)
187940.6455 1
< 0.1%
159387.9233 1
< 0.1%
135693.1177 1
< 0.1%
133846.7488 1
< 0.1%
113174.6798 1
< 0.1%
103858.4408 1
< 0.1%
101504.2655 1
< 0.1%
93940.94392 1
< 0.1%
93496.81223 1
< 0.1%
92153.92008 1
< 0.1%

job_stability_years
Real number (ℝ)

High correlation 

Distinct5644
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.458007
Minimum0.0082191781
Maximum46.736986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:15.200366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0082191781
5-th percentile1.4328767
Q15.0164384
median8.7315068
Q312.843836
95-th percentile29.363836
Maximum46.736986
Range46.728767
Interquartile range (IQR)7.8273973

Descriptive statistics

Standard deviation7.9758191
Coefficient of variation (CV)0.76265191
Kurtosis1.9868705
Mean10.458007
Median Absolute Deviation (MAD)3.8684932
Skewness1.4594811
Sum250814.39
Variance63.613691
MonotonicityNot monotonic
2025-05-17T17:11:15.553366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.731506849 5774
 
24.1%
6.764383562 143
 
0.6%
1.761643836 99
 
0.4%
1.095890411 88
 
0.4%
2.01369863 75
 
0.3%
3.761643836 73
 
0.3%
2.095890411 70
 
0.3%
2.515068493 70
 
0.3%
2.928767123 69
 
0.3%
1.515068493 68
 
0.3%
Other values (5634) 17454
72.8%
ValueCountFrequency (%)
0.008219178082 1
 
< 0.1%
0.01095890411 3
< 0.1%
0.0301369863 2
< 0.1%
0.04109589041 1
 
< 0.1%
0.07123287671 1
 
< 0.1%
0.07397260274 1
 
< 0.1%
0.08767123288 2
< 0.1%
0.09315068493 4
< 0.1%
0.1232876712 1
 
< 0.1%
0.1287671233 2
< 0.1%
ValueCountFrequency (%)
46.7369863 1
< 0.1%
45.62465753 1
< 0.1%
44.62739726 1
< 0.1%
44.25205479 1
< 0.1%
42.95616438 1
< 0.1%
42.65205479 1
< 0.1%
42.62739726 1
< 0.1%
42.24657534 1
< 0.1%
42.04109589 1
< 0.1%
42.01643836 1
< 0.1%

address_stability_years
Real number (ℝ)

Distinct8368
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.788653
Minimum0.10958904
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:15.901367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10958904
5-th percentile3.4328767
Q111.039726
median23.224658
Q336.164384
95-th percentile55.950137
Maximum60
Range59.890411
Interquartile range (IQR)25.124658

Descriptive statistics

Standard deviation15.859222
Coefficient of variation (CV)0.63977746
Kurtosis-0.69618393
Mean24.788653
Median Absolute Deviation (MAD)12.457534
Skewness0.47533722
Sum594506.27
Variance251.51491
MonotonicityNot monotonic
2025-05-17T17:11:16.299370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 730
 
3.0%
16.77260274 370
 
1.5%
26.77808219 252
 
1.1%
6.764383562 247
 
1.0%
36.78630137 239
 
1.0%
23.22465753 209
 
0.9%
3.761643836 199
 
0.8%
8.767123288 177
 
0.7%
11.76712329 175
 
0.7%
21.77534247 166
 
0.7%
Other values (8358) 21219
88.5%
ValueCountFrequency (%)
0.1095890411 1
< 0.1%
0.2712328767 1
< 0.1%
0.3506849315 2
< 0.1%
0.4547945205 2
< 0.1%
0.4602739726 1
< 0.1%
0.4712328767 1
< 0.1%
0.498630137 1
< 0.1%
0.5780821918 1
< 0.1%
0.597260274 2
< 0.1%
0.6246575342 1
< 0.1%
ValueCountFrequency (%)
60 730
3.0%
59.9890411 1
 
< 0.1%
59.98356164 1
 
< 0.1%
59.96712329 1
 
< 0.1%
59.96164384 1
 
< 0.1%
59.95890411 1
 
< 0.1%
59.92876712 1
 
< 0.1%
59.92054795 2
 
< 0.1%
59.91780822 1
 
< 0.1%
59.91506849 1
 
< 0.1%

job_stability_missing
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.5 KiB
0
18212 
1
5771 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23983
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 18212
75.9%
1 5771
 
24.1%

Length

2025-05-17T17:11:16.694367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:16.858367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 18212
75.9%
1 5771
 
24.1%

Most occurring characters

ValueCountFrequency (%)
0 18212
75.9%
1 5771
 
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18212
75.9%
1 5771
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18212
75.9%
1 5771
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18212
75.9%
1 5771
 
24.1%

address_stability_missing
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.5 KiB
0
23775 
1
 
208

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23983
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23775
99.1%
1 208
 
0.9%

Length

2025-05-17T17:11:17.061367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T17:11:17.219367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23775
99.1%
1 208
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 23775
99.1%
1 208
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23775
99.1%
1 208
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23775
99.1%
1 208
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23775
99.1%
1 208
 
0.9%

cust_income_log
Real number (ℝ)

High correlation 

Distinct18823
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7334292
Minimum0
Maximum10.155915
Zeros85
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-05-17T17:11:17.467366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.1213589
Q15.3627209
median5.6641948
Q36.0090595
95-th percentile6.7520411
Maximum10.155915
Range10.155915
Interquartile range (IQR)0.64633861

Descriptive statistics

Standard deviation0.6431644
Coefficient of variation (CV)0.11217796
Kurtosis25.070101
Mean5.7334292
Median Absolute Deviation (MAD)0.31859716
Skewness-1.9857184
Sum137504.83
Variance0.41366045
MonotonicityNot monotonic
2025-05-17T17:11:17.805366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.445742182 217
 
0.9%
5.732345013 191
 
0.8%
5.599276295 186
 
0.8%
5.26428318 178
 
0.7%
5.849768042 121
 
0.5%
5.510011002 105
 
0.4%
5.95484046 105
 
0.4%
5.681222202 93
 
0.4%
0 85
 
0.4%
5.377057451 84
 
0.4%
Other values (18813) 22618
94.3%
ValueCountFrequency (%)
0 85
0.4%
0.3254224007 1
 
< 0.1%
0.5705448583 13
 
0.1%
2.162438461 2
 
< 0.1%
4.305249423 1
 
< 0.1%
4.411147514 1
 
< 0.1%
4.474859973 1
 
< 0.1%
4.549994059 1
 
< 0.1%
4.550659576 1
 
< 0.1%
4.569144364 1
 
< 0.1%
ValueCountFrequency (%)
10.15591492 1
< 0.1%
10.12484117 1
< 0.1%
9.911786971 1
< 0.1%
9.74894622 1
< 0.1%
9.490648593 1
< 0.1%
9.461902718 1
< 0.1%
9.352944426 1
< 0.1%
9.258512925 1
< 0.1%
9.211134625 1
< 0.1%
9.022892467 1
< 0.1%

Interactions

2025-05-17T17:11:04.979255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:48.910598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:51.608598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:53.727599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:56.038255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:58.098257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:00.257256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:02.836255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:05.280256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:49.332597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:51.877598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:54.063599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:56.328255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:58.390255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:00.511255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:03.103252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:05.592254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:49.722594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:52.132595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:54.351598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:56.585254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:58.670254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:00.758256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:03.359257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:05.870254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:50.083599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:52.402598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:54.645598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:56.852254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:58.948256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:01.036256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:03.621253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:06.137254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:50.436599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:52.653594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:54.917411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:57.091255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:59.208255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:01.277256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:03.903257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:06.421252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:50.753594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:52.925597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:55.218254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:57.354255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:59.472254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:02.110255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:04.193253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:06.676256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:51.018596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:53.168598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:55.474254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:57.592255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:59.722256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:02.341257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:04.436253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:06.914254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:51.299599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:53.448597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:55.734254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:57.850255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:10:59.988256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:02.604254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T17:11:04.706257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-17T17:11:18.075640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
address_stability_missingaddress_stability_yearsagecocunutcurrent_balance_eurcust_incomecust_income_logeducationemploymentgenderjob_stability_missingjob_stability_yearsmarital_statusmortgage_ynyears_with_bank
address_stability_missing1.0000.2780.1550.0640.0450.0000.1560.0160.1100.0150.0920.0630.0200.0000.036
address_stability_years0.2781.0000.3070.023-0.028-0.092-0.0920.0780.1580.1980.2170.1670.1030.0740.012
age0.1550.3071.000-0.029-0.045-0.100-0.1000.0710.4860.0840.7350.3890.3110.0690.223
cocunut0.0640.023-0.0291.0000.552-0.058-0.0580.0710.0520.0440.076-0.0510.0370.344-0.231
current_balance_eur0.045-0.028-0.0450.5521.0000.1280.1280.0740.0580.0180.0420.0170.0000.8580.038
cust_income0.000-0.092-0.100-0.0580.1281.0001.0000.0670.0460.0130.0260.2010.0000.1290.308
cust_income_log0.156-0.092-0.100-0.0580.1281.0001.0000.1820.1780.0920.2520.2010.0600.1770.308
education0.0160.0780.0710.0710.0740.0670.1821.0000.1090.1150.0830.0600.0480.1030.129
employment0.1100.1580.4860.0520.0580.0460.1780.1091.0000.0830.8790.4200.2060.0910.083
gender0.0150.1980.0840.0440.0180.0130.0920.1150.0831.0000.0420.0680.2040.0160.026
job_stability_missing0.0920.2170.7350.0760.0420.0260.2520.0830.8790.0421.0000.6780.2920.0480.063
job_stability_years0.0630.1670.389-0.0510.0170.2010.2010.0600.4200.0680.6781.0000.1700.0470.288
marital_status0.0200.1030.3110.0370.0000.0000.0600.0480.2060.2040.2920.1701.0000.0160.077
mortgage_yn0.0000.0740.0690.3440.8580.1290.1770.1030.0910.0160.0480.0470.0161.0000.063
years_with_bank0.0360.0120.223-0.2310.0380.3080.3080.1290.0830.0260.0630.2880.0770.0631.000

Missing values

2025-05-17T17:11:07.314253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-17T17:11:07.786255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cocunutmortgage_ynageyears_with_bankmarital_statuseducationemploymentgendercust_incomecurrent_with_bank_datecurrent_balance_eurjob_stability_yearsaddress_stability_yearsjob_stability_missingaddress_stability_missingcust_income_log
01Y5213MHGHPVEM909.5013082004-03-167648.3506926.76438437.493151006.813995
19Y4911MHGHMISCM288.4615392005-11-0730189.90492012.71780816.816438005.668022
211Y5514MBCRSTEM1280.5286922003-06-2550553.17454026.11232911.279452007.155809
312Y6610MBCRMISCF620.9597692006-12-2115907.2833808.73150715.797260106.432875
418Y479SMASPVEF2239.8538462007-08-0727916.1926202.7835627.183562007.714612
519Y3510MBCRSTEF1016.0043082006-08-0226571.47200010.18630123.224658016.924617
621Y5310MHGHSTEF458.6092312006-10-2721949.50400031.83287723.778082006.130377
724Y5810MHGHPVEF615.3846152007-06-2750387.55885011.04931518.528767006.423871
829Y4714MBCRPVEM744.0076152002-11-168731.9489236.4986309.101370006.613394
933Y4612MHGHPVEM473.8180002004-11-2416084.14246025.0301378.863014006.162932
cocunutmortgage_ynageyears_with_bankmarital_statuseducationemploymentgendercust_incomecurrent_with_bank_datecurrent_balance_eurjob_stability_yearsaddress_stability_yearsjob_stability_missingaddress_stability_missingcust_income_log
2397379961N5610DBCRSTEF481.6268462006-10-141088.56469227.36438449.794521006.179244
2397479965N5011MOTHPVEM197.3615382006-01-123059.95046220.02465848.964384005.290091
2397579970N5113MHGHSTEF242.0436922003-07-222288.70015419.93972628.780822005.493241
2397679974N484MHGHPVEF247.1922312013-04-126376.92307710.7260275.087671005.514204
2397779976N554WHGHRETF176.1457692013-02-253347.6545388.73150738.117808105.176973
2397879979N673MBCRRETM179.2163082014-06-02781.1900008.73150717.586301105.194158
2397979982N5913MHGHPVEF690.0951542004-01-1213716.36638012.76986319.772603006.538278
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